TY - CHAP A1 - Schmitt, Ingo ED - Aerts, Diederik ED - Khrennikov, Andrei ED - Melucci, Massimo ED - Bourama, Toni T1 - Incorporating Weights into a Quantum-Logic-Based Query Language T2 - Quantum-Like Models for Information Retrieval and Decision-Making N2 - Traditional database query languages are based on set theory and crisp first order logic. However, many applications require imprecise conditions which return result objects associated with a degree of fulfillment. For example, a research paper should be reviewed by giving a degree of fulfillment for originality and relevance. Very often, imprecise conditions of a query are of different importance. Thus, a query language should allow the user to give conditions different weights. This paper proposes a weighting approach which is realized by means of conjunction, disjunction, and negation. Thus, our weighting is completely embedded within a logic. As a result, logical rules are preserved and can be used for query reformulation and optimization. As underlying logic-based query language, we use the CQQL query language. Furthermore we demonstrate that our weighting approach is applicable to further logic-based query languages. KW - Weights KW - Database query language KW - Information retrieval KW - DB&IR Y1 - 2019 UR - https://www.springer.com/de/book/9783030259129 SN - 978-3-030-25912-9 SN - 978-3-030-25913-6 U6 - https://doi.org/10.1007/978-3-030-25913-6_7 SN - 2520-1948 SP - 129 EP - 143 PB - Springer Nature Switzerland AG, Springer International Publishing CY - Cham ET - 1. Auflage ER - TY - GEN A1 - Schmitt, Ingo T1 - QLC: A Quantum-Logic-inspired Classifier T2 - IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium N2 - Besides a good prediction a classifier is to give an explanation how the input data is related to the classification result. There is a general agreement that logic expressions provide a better explanation than other methods like SVM, logistic regression, and neural networks. However, a classifier based on Boolean logic needs to map continuous data to Boolean values which can cause a loss of information. In contrast, we design a quantum-logic-inspired classifier where continuous data are directly processed and the laws of the Boolean algebra are maintained. As a result from our approach we obtain a CQQL condition which provides good insights into the relation of input features to the class decision. Furthermore, our experiment shows a good prediction accuracy. Y1 - 2022 SN - 9781450397094 U6 - https://doi.org/10.1145/3548785.3548790 SP - 120 EP - 127 PB - Association of Computing Machinery CY - New York ER - TY - GEN A1 - Schmitt, Ingo T1 - QLDT: A Decision Tree Based on Quantum Logic T2 - New Trends in Database and Information Systems. ADBIS 2022 N2 - Besides a good prediction a classifier is to give an explanation how input data is related to the classification result. Decision trees are very popular classifiers and provide a good trade-off between accuracy and explainability for many scenarios. Its split decisions correspond to Boolean conditions on single attributes. In cases when for a class decision several attribute values interact gradually with each other, Boolean-logic-based decision trees are not appropriate. For such cases we propose a quantum-logic inspired decision tree (QLDT) which is based on sums and products on normalized attribute values. In contrast to decision trees based on fuzzy logic a QLDT obeys the rules of the Boolean algebra. KW - Quantum Logic KW - Decission tree KW - Interpretable AI Y1 - 2022 SN - 978-3-031-15743-1 SN - 978-3-031-15742-4 U6 - https://doi.org/10.1007/978-3-031-15743-1_28 SP - 299 EP - 308 PB - Springer CY - Switzerland ER - TY - GEN A1 - Huber-Liebl, Markus A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Schmitt, Ingo A1 - beim Graben, Peter A1 - Wolff, Matthias T1 - Quantum-inspired Cognitive Agents T2 - Frontiers in Applied Mathematics and Statistics N2 - The concept of intelligent agents is—roughly speaking—based on an architecture and a set of behavioral programs that primarily serve to solve problems autonomously. Increasing the degree of autonomy and improving cognitive performance, which can be assessed using cognitive and behavioral tests, are two important research trends. The degree of autonomy can be increased using higher-level psychological modules with which needs and motives are taken into account. In our approach we integrate these modules in architecture for an embodied, enactive multi-agent system, such that distributed problem solutions can be achieved. Furthermore, after uncovering some weaknesses in the cognitive performance of traditionally designed agents, we focus on two major aspects. On the one hand, the knowledge processing of cognitive agents is based on logical formalisms, which have deficiencies in the representation and processing of incomplete or uncertain knowledge. On the other hand, in order to fully understand the performance of cognitive agents, explanations at the symbolic and subsymbolic levels are required. Both aspects can be addressed by quantum-inspired cognitive agents. To investigate this approach, we consider two tasks in the sphere of Shannon's famous mouse-maze problem: namely classifying target objects and ontology inference. First, the classification of an unknown target object in the mouse-maze, such as cheese, water, and bacon, is based on sensory data that measure characteristics such as odor, color, shape, or nature. For an intelligent agent, we need a classifier with good prediction accuracy and explanatory power on a symbolic level. Boolean logic classifiers do work on a symbolic level but are not adequate for dealing with continuous data. Therefore, we demonstrate and evaluate a quantum-logic-inspired classifier in comparison to Boolean-logic-based classifiers. Second, ontology inference is iteratively achieved by a quantum-inspired agent through maze exploration. This requires the agent to be able to manipulate its own state by performing actions and by collecting sensory data during perception. We suggest an algebraic approach where both kinds of behaviors are uniquely described by quantum operators. The agent's state space is then iteratively constructed by carrying out unitary action operators, while Hermitian perception operators act as observables on quantum eigenstates. As a result, an ontology emerges as the simultaneous solution of the respective eigenvalue equations. Tags: btuktqiai; btuktcogsys; btukttheseus; btuktqtheseus Y1 - 2022 UR - https://www.frontiersin.org/articles/10.3389/fams.2022.909873 U6 - https://doi.org/10.3389/fams.2022.909873 SN - 2297-4687 IS - 8 SP - 1 EP - 31 ER - TY - GEN A1 - Schmitt, Ingo T1 - QLDT+: Efficient Construction of a Quantum Logic Decision Tree T2 - ICMLT '23: Proceedings of the 2023 8th International Conference on Machine Learning Technologies, SESSION: Session 2 - Data Model Design and Algorithm Analysis N2 - The quantum-logic inspired decision tree (QLDT) is based on quantum logic concepts and input values from the unit interval whereas the traditional decision tree is based on Boolean values. The logic behind the QLDT obeys the rules of a Boolean algebra. The QLDT is appropriate for classification problems where for a class decision several input values interact gradually with each other. The QLDT construction for a classification problem with n input attributes requires the computation of 2n minterms. The QLDT+ method, however, uses a heuristic for obtaining a QLDT with much smaller computational complexity. As result, the QLDT+ method can be applied to classification problems with a higher number of input attributes. Y1 - 2023 UR - https://dl.acm.org/doi/proceedings/10.1145/3589883 SN - 978-1-4503-9832-9 U6 - https://doi.org/10.1145/3589883.3589895 SP - 82 EP - 88 PB - Association for Computing Machinery CY - New York, NY, United States ER - TY - GEN A1 - Stahl, Alexander A1 - Schmitt, Ingo T1 - BBQ-Tree - A Decision Tree with Boolean and Quantum Logic Decisions T2 - Advances in Databases and Information Systems (ADBIS 2024) N2 - This study proposes the BBQ-Tree, a new logic-based classifier that combines the two concepts of classical Decision Trees and Quantum-Logic Decision Trees into a generalized model. It thus creates a method that has the power to solve classification problems that incorporate both curved and linear decision boundaries, with a particular focus on interpretability. In addition to the model itself, ways for its efficient training are discussed. Our experimental evaluation demonstrates that our approach is able to produce models that remain compact and provide good insights over trends in data while maintaining an accuracy not worse than Decision Trees alone. KW - quantum logic KW - classifier KW - interpretable AI KW - decision tree Y1 - 2024 UR - https://link.springer.com/chapter/10.1007/978-3-031-70626-4_14 IS - 14918 SP - 201 EP - 214 PB - Springer ER - TY - GEN A1 - Schmitt, Ingo T1 - Logic interpretations of ANN partition cells T2 - Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI) N2 - Consider a binary classification problem solved using a feed-forward artificial neural network (ANN). Let the ANN be composed of a ReLU layer and several linear layers (convolution, sum-pooling, or fully connected). We assume the network was trained with high accuracy. Despite numerous suggested approaches, interpreting an artificial neural network remains challenging for humans. For a new method of interpretation, we construct a bridge between a simple ANN and logic. As a result, we can analyze and manipulate the semantics of an ANN using the powerful tool set of logic. To achieve this, we decompose the input space of the ANN into several network partition cells. Each network partition cell represents a linear combination that maps input values to a classifying output value. For interpreting the linear map of a partition cell using logic expressions, we suggest minterm values as the input of a simple ANN. We derive logic expressions representing interaction patterns for separating objects classified as 1 from those classified as 0. To facilitate an interpretation of logic expressions, we present them as binary logic trees. KW - XAI, KW - Logic KW - Probability Theory KW - Artificial Neural Networks Y1 - 2024 U6 - https://doi.org/10.48550/ARXIV.2408.14314 IS - abs/2408.14314 ER - TY - BOOK A1 - Wirsching, Günther A1 - Schmitt, Ingo A1 - Wolff, Matthias T1 - Quantenlogik, Band 1 : eine Einführung für Ingenieure und Informatiker N2 - Messungen an Quantenobjekten haben eine logische Struktur. Diese schließt die klassische Logik ein, geht jedoch weit darüber hinaus. Das wesentliche Ziel dieses Lehrbuchs ist es, die mathematischen Werkzeuge der Quantenlogik zu vermitteln und technische Anwendungsmöglichkeiten aufzuzeigen. Die dafür erforderlichen mathematischen Sachverhalte werden anhand von Beispielen so erläutert und motiviert, dass sie für angehende Ingenieure und Informatiker verständlich sind. Die vorliegende Auflage wurde korrigiert und um zahlreiche Anwendungen, zusätzliche Beispiele und Beweise erweitert sowie um ein neues Beispiel zur Mustererkennung ergänzt. KW - Projektive Geometrie KW - Lineare Algebra KW - Verbandstheorie KW - Information Retrieval KW - Kognitive Systeme KW - Künstliche Intelligenz KW - Vektor-symbolische Architekturen KW - Quantenmessung KW - Wahrscheinlichkeiten KW - Anfragesysteme KW - Informationskodierung KW - Quantenlogik KW - Logik KW - Mathematische Strukturen KW - Logik der Orthogonalprojektoren KW - Quantenregister KW - Quantenbit KW - Qubits KW - Fuzzy-Logik KW - Skalarproduktraum Y1 - 2025 UR - https://link.springer.com/book/10.1007/978-3-662-71335-8 SN - 978-3-662-71335-8 SN - 978-3-662-71334-1 U6 - https://doi.org/10.1007/978-3-662-71335-8 PB - Springer Vieweg CY - Berlin ; Heidelberg ET - 2. Auflage ER - TY - BOOK A1 - Wirsching, Günther A1 - Wolff, Matthias A1 - Schmitt, Ingo T1 - Quantenlogik : eine Einführung für Ingenieure und Informatiker N2 - Das wesentliche Ziel dieses Lehrbuchs ist es, die mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse auf der Grundlage der klassischen Logik und der Quantenlogik zu entwickeln. Die dafür erforderlichen mathematischen Sachverhalte werden so dargestellt und anhand von Beispielen motiviert, dass sie für angehende Ingenieure und Informatiker verständlich sind. - Kompakte logische Darstellung der mathematischen Werkzeuge zur Modellierung kognitiver Strukturen und Prozesse - Beispiele erläutern die Anwendung im Engineering Zielgruppen sind insbesondere Studierende der Ingenieurwissenschaften und der Informatik, aber auch Studierende der Mathematik oder der Physik können durch den anwendungsbezogenen Blick ihren Horizont erweitern. KW - Projektive Geometrie KW - Lineare Algebra KW - Verbandstheorie KW - Information Retrieval KW - Kognitive Systeme KW - Künstliche Intelligenz KW - vektor-symbolische Architekturen KW - Quantenmessung KW - Wahrscheinlichkeiten KW - Anfragesysteme KW - Informationskodierung Y1 - 2023 UR - https://link.springer.com/book/10.1007/978-3-662-66780-4 SN - 978-3-662-66779-8 SN - 978-3-662-66780-4 U6 - https://doi.org/10.1007/978-3-662-66780-4 PB - Springer Vieweg CY - Berlin ; Heidelberg ET - 1. Auflage ER - TY - GEN A1 - Gupta, Vipul A1 - Paul, Jonathan David Heaton A1 - Schmitt, Ingo A1 - Pyczak, Florian T1 - SciLitMiner : an intelligent system for scientific literature mining and knowledge discovery T2 - Advanced intelligent systems N2 - Recent advances in data mining have enabled automation in literature‐based discovery (LBD), allowing synergistic evaluation of experimental findings reported in scientific publications. However, existing tools and digital libraries fall short in generating relevant literature collections and evaluating them for highly specific questions. This article presents SciLitMiner, an intelligent system to address this gap. SciLitMiner enables federated ingestion of literature from digital libraries; applies advanced retrieval techniques, including dataset‐aware retrieval from visual elements, to identify relevant studies; and leverages retrieval‐augmented generation (RAG) tailored to domain‐specific knowledge reasoning. The system is applied in materials science to study the creep behavior of γ‐TiAl alloys, revealing the intricate interplay between material, process, microstructure, and creep rate, represented through knowledge graphs. Two domain experts rate responses from the knowledge reasoning workflow with OpenAI large language models (LLMs) as the backbone above “good” (3 on a 5‐point Likert scale) in over 90% across qualitative criteria, indicating strong performance. In a case study, the workflow also outperforms gpt‐4.5‐turbo with web search and other leading tools in reliability. A second case study benchmarks open‐source LLMs as drop‐in replacements for proprietary models, demonstrating comparable‐to‐superior performance. The system's flexibility enables its use in automated LBD across diverse research domains. Y1 - 2025 U6 - https://doi.org/10.1002/aisy.202501235 SN - 2640-4567 SP - 1 EP - 20 PB - Wiley CY - Weinheim ER - TY - GEN A1 - Schmitt, Ingo A1 - Sowoidnich, Kay A1 - Gosswami, Tapashi A1 - Sumpf, Bernd A1 - Maiwald, Martin A1 - Wolff, Matthias T1 - PCA-based peak feature selection for classification of spectroscopic datasets T2 - Journal of chemometrics N2 - Reducing feature dimensionality in spectroscopic data is crucial for efficient analysis and classification. Using all available features for classification typically results in an unacceptably high runtime and poor accuracy. Popular feature extraction methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and autoencoders, reduce feature dimensionality by extracting latent features that can be challenging to interpret. To enable better human interpretation of the classification model, we avoid extraction methods and instead propose applying feature selection methods. In this work, we develop an innovative PCA-based feature selection method for spectroscopic data, providing an essential subset of the original features. As an important advantage, no prior knowledge about the characteristic signals of the respective target substance is required. In this proof-of-concept study, the proposed method is initially characterized using simulated Raman and infrared absorption datasets. From the top five PCA eigenvectors of spectroscopic data, we identify a set of three top peaks each at specific wavenumbers (features). The compact set of selected features is then used for classification tasks applying a decision tree. Based on two well-defined spectroscopic datasets, our study demonstrates that our new method of PCA-based peak finding outperforms selected other approaches with regard to interpretability and accuracy. For both investigated datasets, accuracies greater than 97% are achieved. Our approach shows large potential for accurate classification combined with interpretability in further scenarios involving spectroscopic datasets. KW - Cclassification KW - Decision tree KW - Peak finding KW - Principal component analysis KW - Spectroscopic data Y1 - 2025 U6 - https://doi.org/10.1002/cem.70074 SN - 0886-9383 SN - 1099-128X VL - 39 IS - 11 SP - 1 EP - 14 PB - John Wiley & Sons Ltd. CY - New York, NY ER - TY - GEN A1 - Schmitt, Ingo A1 - Stahl, Alexander T1 - BBQ-Tree : a unified classifier and regressor combining Boolean and quantum logic decisions T2 - Information systems N2 - This article provides a detailed explanation of the BBQ-Tree, a unified logic-based model that integrates both classical Decision Trees and Quantum-Logic Decision Trees into a generalized framework for classification and regression. As it combines these paradigms, the BBQ-Tree effectively addresses problems with both linear and curved decision boundaries while prioritizing interpretability. We provide a detailed description of the underlying concepts, a possible training algorithm, experimental evaluations and the incorporation of regression functionality, broadening its applicability beyond classification tasks. Strategies for efficient training and model optimization are also presented. Experimental results demonstrate that the BBQ-Tree produces compact, interpretable models capable of revealing data trends, while achieving accuracy comparable to Decision Trees. Furthermore, its new regression capabilities highlight its versatility and performance across a wider range of tasks. KW - Quantum logic KW - Classifier KW - Regression KW - Interpretable AI KW - Decision tree Y1 - 2025 U6 - https://doi.org/10.1016/j.is.2025.102632 SN - 0306-4379 VL - 136 SP - 1 EP - 10 PB - Elsevier BV CY - Amsterdam ER -